Sr Governance Engineer

Fresenius Medical Care; Bengaluru; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

Fresenius Medical Care is seeking a Data Governance Engineer to design scalable governance, quality, security, and compliance capabilities across cloud data platforms in AWS and Azure. The role collaborates with data engineering, analytics, AI/ML, and compliance teams to ensure trusted, secure data for analytics, reporting, and GenAI use cases.

The ideal candidate has 5+ years in data governance or related fields, with 3+ years on cloud data platforms, and strong SQL/Python skills.

Qualifications

• 5+ years in data governance, data engineering, or data management roles.
• 3+ years of hands-on experience in cloud data platforms (AWS and/or Azure).
• Experience designing data catalogs, metadata, and lineage.

Responsibilities

• Design and implement data governance frameworks including ownership, stewardship, classification, retention, and lifecycle management.
• Translate regulatory and compliance requirements into technical controls.
• Define data standards, naming conventions, and documentation practices.
• Enable end-to-end data lineage across ingestion, transformation, and consumption layers.
• Implement data catalogs, metadata management, and business glossaries.
• Establish data quality rules, checks, and monitoring in ETL/ELT pipelines.
• Define SLAs and metrics for data reliability and trustworthiness.
• Support secure data access for analytics, ML, and GenAI workloads.

Skills

SQL

Python

Databricks

Snowflake

AWS/Azure

Data catalogs

Data quality

Security & access

ML platforms

Data architecture

ETL/ELT

DevOps

Education

Bachelor’s degree in Computer Science, Data Management, Information Systems, Engineering

Master’s degree is a plus

Tools

Databricks

Snowflake

AWS

Azure

Job description

Role Overview

We are seeking a Data Governance Engineer to design and implement scalable data governance, quality, security, and compliance capabilities across modern cloud data platforms. This role will work closely with data engineering, analytics, AI/ML, and compliance teams to ensure data is trusted, secure, compliant, and ready for analytics, reporting, and GenAI use cases across AWS and Azure environments.

Key Responsibilities

Data Governance Frameworks & Policy Implementation

• Design and implement data governance frameworks covering data ownership, stewardship, classification, retention, and lifecycle management.
• Translate business, regulatory, and compliance requirements into enforceable technical controls.
• Define and manage data standards, naming conventions, and documentation practices.

Metadata, Lineage & Cataloging

• Implement and manage data catalogs, metadata management, and business glossaries.
• Enable end‑to‑end data lineage across ingestion, transformation, and consumption layers.
• Ensure metadata coverage for datasets in Databricks, Snowflake, S3, and ADLS.

Data Quality Engineering

• Design and implement data quality rules, checks, and monitoring frameworks.
• Integrate data quality validation into ETL/ELT pipelines.
• Define SLAs and metrics for data reliability and trustworthiness.
• Support root cause analysis and remediation of data quality issues.

Security, Privacy & Compliance

• Implement data classification, masking, tokenization, and encryption strategies.
• Enforce role‑based and attribute‑based access control across cloud platforms.
• Ensure compliance with regulatory and privacy requirements (e.g., HIPAA, GDPR, internal policies).
• Support secure data access for analytics, ML, and GenAI workloads (SageMaker, Bedrock).

Cloud & Platform Governance

• Define and enforce governance controls across AWS and Azure data services.
• Partner with platform teams to implement guardrails, policies, and controls.
• Monitor and audit data access, usage, and policy compliance.

AI / GenAI Governance Enablement

• Enable responsible AI and GenAI governance by:
• Ensuring trusted, high‑quality data inputs
• Applying secure access and privacy controls
• Supporting lineage and explainability for AI/ML data pipelines
• Collaborate with AI/ML teams leveraging SageMaker and Amazon Bedrock.

Automation & DevOps

• Automate governance, quality, and compliance checks using CI/CD pipelines.
• Implement infrastructure‑as‑code for governance configurations where applicable.
• Integrate monitoring, logging, and alerting for governance controls.

Collaboration & Stakeholder Engagement

• Work closely with data engineers, architects, data scientists, compliance, and business stakeholders.
• Educate teams on governance standards and best practices.
• Assist data owners and stewards with onboarding governed datasets.

Required Skills & Experience

Technical Skills

• Strong experience with SQL and Python.
• Hands‑on experience with Databricks and Snowflake.
• Proven experience with AWS and/or Azure data platforms.
• Experience implementing:
• Data catalogs, metadata, and lineage
• Data quality frameworks
• Data security and access controls
• Familiarity with ML / AI platforms (SageMaker, Bedrock) from a data governance perspective.
• Understanding of data architecture, ETL/ELT workflows, and analytics pipelines.
• Experience with DevOps and automation concepts.

Soft Skills

• Strong analytical and problem‑solving skills.
• Excellent communication skills with both technical and non‑technical audiences.
• Ability to influence data practices across teams.
• Detail‑oriented with a strong focus on risk mitigation and data trust.

Education Requirements

Bachelor’s degree in Computer Science, Data Management, Information Systems, Engineering, or a related field.

Master’s degree is a plus.

Experience Requirements

5+ years of experience in data engineering, data governance, or data management roles.

3+ years of hands‑on experience in cloud data platforms (AWS and/or Azure).

Experience supporting enterprise‑scale analytics, reporting, and AI initiatives.

Preferred Qualifications

• Certifications in AWS, Azure, Databricks, Snowflake, or Data Governance.
• Experience in regulated industries (healthcare, life sciences, finance).
• Familiarity with privacy, security, and compliance frameworks.
• Experience supporting governed data for GenAI initiatives.